How-to Guides

Neural operators extend the concept of neural networks to function mappings, which enables discretization-invariant and mesh-free mappings of data with applications to physics-informed training, super-resolution, and more.

This is a collection of notebooks that showcase some applications of continuiti and serve as a guide to solve specific problems.

Time Series

Operator learning for non-uniform time series

Super-resolution

Neural operators for super-resolution

Physics-informed

Training physics-informed neural operators

Meshes

Reading meshes for operator learning

Self-supervised

Training self-supervised neural operators

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Last update: 2024-08-20
Created: 2024-08-20